TY - THES A1 - Sheryar, Muhammad T1 - Reinforcement learning for building energy system control in multi-family buildings N2 - The demand for heat energy is increasing worldwide and to achieve net zero carbon emissions targets, more innovation is needed for heat production. Heat pumps are considered a potential replacement for boilers and are currently in high demand. The next approach is to optimize the use of heat pumps with household PV production to avoid grid overloading due to running increased demand by the heat pump. In this work, a Reinforcement Learning algorithm is used in the MATLAB RL toolbox with an energy-building model built in MATLAB Simulink Carnot. The energy building model uses a heat pump to charge thermal storage, and a PV system is considered with a typical ON/OFF strategy. This work shows how the RL toolbox has the potential to interact with this energy-building model to optimize the heat pump with a PV system. All suggested agents by the MATLAB RL toolbox are investigated with this building energy model (BEM), and annual simulation is performed with a well-trained agent, which converges during training. Two different models have been developed for heat pump control. The first model is called the RL-based Heat Pump Controller, which is designed to meet thermal targets only. The second model is called the PV- optimized RL-based Heat Pump Controller, which not only meets thermal targets but also considers the operation of the PV system with the heat pump. The simulation results show that using the RL toolbox, the RL-based Heat Pump Controller model has performed excellently. In the PV-optimized RL-based Heat Pump Controller model, there is almost a 4.37% increase in PV self-consumption compared to the typical control strategy, resulting in annual electricity savings of almost 3.52 MWh. Some challenges of using the RL toolbox are also highlighted with future recommendations, which mainly include computational efforts. Y1 - 2024 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-44608 CY - Ingolstadt ER - TY - THES A1 - Ghorreshi, Amirmahdi T1 - Determining the optimal size of Agri-PV and battery systems in rural areas BT - a case study of Morocco and Algeria N2 - The global population is anticipated to experience a significant surge by 2050, posing profound challenges in ensuring food and energy security. Concurrently, the prevalence of hunger, exacerbated by pandemics, climate shocks, and conflicts, underscores the urgency to enhance agricultural productivity and energy efficiency. Agriculture currently accounts for 70% of freshwater consumption and 30% of global energy usage, requiring innovative solutions to meet increasing demands. In this context, Agri-Photovoltaic (Agri-PV) systems emerge as a promising approach, integrating agricultural practices with solar technology to enhance food production and energy generation simultaneously. These systems not only address the pressing need for sustainable food and energy sources but also offer benefits such as resilience against extreme weather events and income diversification for farmers. This study investigates the potential of Agri-PV systems to address power outages in rural areas of Kissane in Morocco, as well as Bouda in Algeria. These areas are characterized by weak grid networks. After developing the load profiles of farms and households in the case study areas based on real data and literature, the PV-SOL software is utilized to estimate the size of the photovoltaic system. After simulation in the software and considering the type of Agri-PV substructure, three different scenarios for sizing the system are taken into account. The first scenario aims to cover all energy demands for both farms and households, the second focuses solely on farms, and the third focuses only on household energy requirements. The results reveal that with six hours of power outage in a day, Kissane requires a 39 kWp Agri-PV system with an 85.31 kWh battery to fulfill its total energy demands. For farms alone, a 38 kWp system with an 82.04 kWh battery is necessary, while households, due to cooperative arrangements and few houses, need only a 1 kWp system with a 3.27 kWh battery. In Bouda, facing six power outages in a day, a 46 kWp system with a 144.34 kWh battery is required for total energy, 41 kWp with a 125.78 kWh battery for farms, and 5 kWp with an 18.56 kWh battery for households. Economic analysis favors Morocco due to higher electricity prices, yielding Kissane a positive 8.69% internal rate of return (IRR) and a competitive 10.95 cent/kWh levelized cost of electricity (LCOE). In contrast, Bouda faces economic challenges with a -2.93% IRR and a 13.77 cent/kWh LCOE, necessitating financial incentives for viability. Keywords: Agri-Photovoltaic (Agri-PV), power outage, load profile, grid network, rural energy security, Morocco, Algeria Y1 - 2024 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-47614 CY - Ingolstadt ER - TY - THES A1 - Dhavale, Aditya Bhalchandra T1 - Study on potential of energy optimization in German dairy industry N2 - The dairy farms form a vital part of German agriculture and consume significant energy for their operation. Germany is working towards energy transition to transform the existing fossil fuel-nuclear system to a sustainable energy system that relies on renewable sources due to various societal and political demands. As the energy demand is increasing due to the rise in population, there is also a growing push in Germany to adopt energy efficient technologies and measures to reduce energy waste. There is a scope for improving the energy efficiency in dairy farms. Using energy optimization techniques can help reduce the load on energy suppliers as well as increase cost savings for farms. German farmers are adopting newer digital technologies to achieve optimization of energy in their farms. One of the key tools in realizing energy optimization is the Energy Management System (EMS). The use of EMS in the case of dairy farms has been reviewed and presented in this work. The objective of the study is to investigate the potential of energy optimization in dairy farms. Efficiency measures like replacing inefficient farm equipment are necessary to achieve energy efficiency. These measures are discussed in detail and their potential to save energy in dairy farms is presented. A comparison of all these measures is studied and how much energy savings can be obtained is reviewed. The use case scenario of EMS in different applications is presented as an indication of possibilities for energy savings using EMS. Furthermore, how the AI technology in combination with EMS can be used in farms and its benefits in dairy farming is reviewed. The types of farms on which the energy optimization measures can be implemented in an ideal case scenario are reviewed. The result of the study shows that there is vast potential for energy optimization in German dairy farms. Thus, the presented work can be further studied to identify further optimization possibilities with the advancement of AI and newer technologies. Y1 - 2024 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-46977 CY - Ingolstadt ER - TY - THES A1 - Baluev, Igor T1 - Design and performance evaluation of a nearly zero-energy office building in a cold climate N2 - Energy independence of commercial buildings is a critical aspect of modern construction. In the context of a planned office building project in Ekibastuz, Kazakhstan, located in an extreme climate with unstable coal energy tariffs, it is necessary to determine the energy needs and understand how to cover them. This thesis focuses on determining the level of efficiency in the use of electricity, reducing dependence on local energy suppliers, and integrating alternative sources. The energy assessment was carried out using software tools such as Autodesk Revit and DesignBuilder, which allow the integration of building information modeling (BIM), building energy modeling (BEM) and energy supply modeling (ESM) to optimize the energy design. The results of commercial projects aimed at achieving nearly zero-energy building (nZEB) standards have informed the development of two energy concepts, both aiming to decrease dependency on district heating networks. These concepts encompass electrical and thermal concepts. In the electrical concept, the air-water heat pump (AWHP) and electric boiler demonstrated better energy efficiency compared to the thermal concept, which utilized a biomass boiler in conjunction with solar thermal collectors (STC) to mitigate the impact of coal-fired electricity. In addition to reducing energy dependence in the air handling unit (AHU), it is proposed to introduce a ground water-to-water heat pump (GWWHP), which is connected to the surface exchanger to capture heat leaks from central heating networks. A comparative analysis of installations without and with heating recovery unit (HRU) shows a fivefold reduction in electricity consumption for heating supply air. Solar technologies, including STC and photovoltaic (PV) panels, are integrated into each concept, but the limited use of STC makes a PV system the preferred option. The decisions made during the modeling process resulted in a fourfold reduction in the office building's average annual consumption compared to government requirements. Selection of energy efficient equipment and modeling led to nZEB classification for both concepts, achieving renewable energy system (RES) penetration rate from 16% to 33% of total consumption. Economic analysis using the annuity method highlights the cost-effectiveness of rooftop PV systems compared to façade PV systems. In addition, wood pellets and solar heat help reduce costs in the thermal concept. The environmental analysis shows the significant reduction in carbon dioxide emissions achieved by the thermal concept, highlighting the importance of alternative energy sources for sustainable urban development. Y1 - 2023 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-45192 CY - Ingolstadt ER -